{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working With Open Data Part 5:  Meteorite Landings and Falls Part 2\n",
    "\n",
    "Now that we've learned how to use Jupyter notebooks to create maps using geo-spacial data, and how create interactive widgets, let's dive into some data analysis. This is a rather rich data set, and we may be able to draw some interesting conclusions from the data. To begin, we have to first gather our libraries and data set in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "This is exactly what we did in the previous notebook: just getting the data again\n",
    "'''\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# numerical python library\n",
    "import numpy as np\n",
    "url = 'https://github.com/fleiser/Meteorite-landings/raw/master/Meteorite_Landings.csv'\n",
    "landings = pd.read_csv(url)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Deeper Data Analysis\n",
    "\n",
    "This data set contains more than the geo-location of meteorite falls. It also contains information about if they were found or not, and quantifications such as the meteorite type and the mass . As such, it is of interest to explore these results  further, perhaps there are interesting trends within the data - waiting for us to discover them. \n",
    "\n",
    "\n",
    "First things first; let's calculate the percentage of meteorites  found vs the total meteorites that fell\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of fallen meteorites found: 97.57847533632287 %\n"
     ]
    }
   ],
   "source": [
    "a = len(landings[landings['fall'] == \"Fell\"])\n",
    "b = len(landings[landings['fall'] == \"Found\"])\n",
    "print(\"Percentage of fallen meteorites found:\", b/(a+b) * 100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, it appears that the majority of meteorites that fall are actually found. Do the meteorites left undiscovered have any distinguishing properties? There are external factors such as the geography or isolation where the meteorite fell making it difficult to find. But, perhaps there are also some internal factors that contribute to their difficulty to find? The data contains several properties such as the mass and type of each meteorite. Using these properties, lets create some visualizations to try and deduce any properties that may dictate if a meteorite is located or not.\n",
    "\n",
    "## Histograms\n",
    "\n",
    "In this case, a potential quantity of interest is the mass of the meteorites that fall. Perhaps there is some relationship between how massive a meteorite, and how likely it is to be found? Let's create a histogram of the masses of both \"Found\" and \"Fell\" meteorites. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e1acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This filters down our data frame to just rows where 'fall' is 'Fell' or \"Found\", and then\n",
    "# by typing ['mass (g)'] after wards, we're only grabbing the mass column and assigning \n",
    "# them to a new variable. \n",
    "\n",
    "mass_fell = landings[landings['fall'] == \"Fell\"]['mass (g)']\n",
    "mass_found = landings[landings['fall'] == \"Found\"]['mass (g)']\n",
    "\n",
    "# Here we're dropping any potential NaN values we've seen before from our columns to prevent \n",
    "# any errors when plotting. To see the error that will show, simply remove the .dropna() \n",
    "\n",
    "mass_fell = mass_fell.dropna()\n",
    "mass_found = mass_found.dropna()\n",
    "\n",
    "# Make a list of data to plot\n",
    "plot_data = [mass_found, mass_fell]\n",
    "\n",
    "\n",
    "'''\n",
    "Here we create a histogram. \n",
    "\n",
    "bins  : This key word specifies how many bins to put the data in for the histogram\n",
    "\n",
    "label : This is to specify the labels for each of the bars in the histogram.  \n",
    "'''\n",
    "\n",
    "# This is another way of setting the figure size. \n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.hist(plot_data, bins = 50, label = [\"Found\", \"Fell\"])\n",
    "\n",
    "plt.xlabel(\"Mass (g)\", size = 16)\n",
    "plt.ylabel(\"Counts\", size = 16)\n",
    "# Uncomment the line below to see a few more bars in the histogram by changing the y axes range.\n",
    "# plt.ylim([0,10])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that's a peculiar histogram that doesn't tell us much. Unfortunately, this is a consequence of having a large range of meteorite masses. We have some incredibly massive meteorites, but we also have a great deal more  small mass meteorites. The spread in values of meteorite mass makes it difficult to bin the data to create a histogram. However, that's something we can absolutely deal with! Almost any time you're dealing with data with a range too large to bin  take the logarithm of the data  to \"squish\" the data into a more condensed range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log10\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Note log10 is log base ten. Simply typing 'log' will be the natural logarithm \n",
    "# The other logarithm included in numpy is log2 for log base two. Any other logarithms\n",
    "# (in the event that you need them) will have to be calculated using properties of logarithms. \n",
    "\n",
    "mass_fell_log = np.log10(landings[landings['fall'] == \"Fell\"]['mass (g)'].dropna())\n",
    "mass_found_log = np.log10(landings[landings['fall'] == \"Found\"]['mass (g)'].dropna())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uh oh! We got a runtime warning. Specifically, a divide by zero encountered in `log10`. This error is telling us is that we have submitted \"bad\" values into the logarithm; in particular there are some meteorites with zero mass. In this case, we'll filter those out by adding another case to our filter where we're finding \"Fell\" and \"Found\" meteorites. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we're simply saying that the mass of the meteorite should also be greater than zero!\n",
    "\n",
    "mass_fell_log = np.log10(landings[(landings['fall'] == \"Fell\") &\n",
    "                                  (landings[\"mass (g)\"] > 0)]['mass (g)'].dropna())\n",
    "\n",
    "mass_found_log = np.log10(landings[(landings['fall'] == \"Found\") &\n",
    "                                   (landings[\"mass (g)\"] > 0)]['mass (g)'].dropna())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful! By excluding meteorites with no recorded mass, we've fixed our error. Now, let's get to plotting these two quantities in a histogram now that we've taken the logarithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e1a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_for_plot = [mass_fell_log, mass_found_log]\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "'''\n",
    "stacked = True : Tells Python we want these plots \"on top of eachother\". Feel free to change it to\n",
    "                 False to see the difference! \n",
    "'''\n",
    "plt.hist(data_for_plot, \n",
    "         bins = 20,\n",
    "         stacked = True,\n",
    "         label = [\"Fell\", \"Found\"])\n",
    "\n",
    "plt.ylabel( \"Counts\", size = 18)\n",
    "plt.xlabel(\"Mass of Meteorite (log$_{10}$(grams))\", size = 18)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where we see that there seems to be some differences in the distributions. Due to difference the scale of our counts, its difficult to compare the two distributions. Not to worry! We simply have to convert from \"counts\" to \"percentages\" for each to put them on the same scale. This can be considered a form of normalization. To do that, we have to use a few more hidden arguments of the histogram `hist` function from `matplotlib`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1dHq+MPyx62SsXldA6Z+emRdn5lsoyfkLKfPQqmEmpOqFYyitnrvnwtM1dTK7+juldWFETKRM+UPb8nUi4qUd6lmFcrpqHmXQBBGx8yCX+BtovVjsCa0jYhPKtC1bUD5IFxk80Fa+6/grD9A5CeyFzShTy7TGtw/ly2UWJUEaMNAP7T1t5V9KOVXXycDp0Y1qxHQmpT/uF1oT2YhYG/hES5nR8L+UlqEjI6J9irJPA6sC/zvC/rrdOIzyvnlzZg7XingJJXn/l4h4facCEbFD9WNpwKDHo+oecDZlWqJPdPoREREvrF7vAwaOw2dat1P9/9nq7teHeR7deJDSyjfY6+hL1d+vRcQiA4wiYpWI2L7LbZ1E+cGxE3BGdWq+ta6VKK3ZO1GmbfvSIjUUn4iINVoetyLwmeru/7Qs7+ozaoTHp1sXU1ocD6bMSgCd+y/Prv5OadvuNjw7YKvdSD8DYJReVxGxQkTs2GH5cjw7INWLHSwB7EOqxVad+ur2ChzXU0ZBvjkifkE5db0upWXgNhbtn7U+8Ktq8NRvKKfYVgXeQDnV+p8tSfB/Ulrbfk75MH2KMr/paymjss+t8bRWbxlMsCzl9NrLKQMqlqFMu/KOLpKVOvED/BA4oBoIdQMlYfppZv60RuyDuRL4j4jYkzJIZ2Ae0ieAQ9sGPF1CmQrqwCiXVryO8iUzMAfnWzrU/0NKH8eLq75ZjwN3Zua3hojpi5Rjvw/w6+pxK1f1TAQ+n5kzRvh8h5SZsyPiQ5QfQjdUgx3mUhL0HSjzon5siCoWd/u/q7bRTdmnI+LNlPlHL6/eOzdSvkg3pPQzfAGlm8HAl+uPKa1sn4mIrahavTNzYHaCIyg/Uo6nzCYwg9IX8vmUwTKvpEzJ88fqcedUP2DeAtwcEd+lJI77Uk7tnle1/i+WzHwkIq4DXhURZ1NGhs+nnFX5TWb+MCI+Tkn4/lC9Zv5I+YG3MeX4zaDM8dvNtvagzAl6KGUO0yso/TDXpQzmWY+yr984xEDEWyn7pHUe0hdSfry2vv7rfEbVOj7dyszHI+ICyjR776ckkZd3KPpNyoCmL0fEayifB5tRPrsupvSNb/fD6jFfqwZw/p1y1btTh4hntF9XK1Hm6Z1FmdT/Tko3md0o+/HSzOz5WRCNQC4Bc095W3putMxD2kXZweYhXZPSoX42JRm6nXLKamUWncdzdeCTlFMrcyiDn+6hjMg8EIiWsm+hdBn4A2V05cOUVo0TgHW6jHkSz85xN3B7gjLx9rXAKcBOQzx+xPFX5SdS+nLdR/kSXjDfJ8PMz1iVmc7g85Aex7NXanqY8mXxA9qu8tPyuA0pfcX+Skkur6cksFNa42opP6E6jnfw7JyN0wfbNy3LV6ScEr2p2s7fKQlFp6vRDLkPOj3/Lo7566r98GB1fGZRplFa5Io/9GAe0i7KD3WlpomUVqObKInnI9Xr/UJKi9eybeUPpiRTjw+8ntvWL09JfH5B6cv3JOXH5Q8pAwzb58ldhpLEzKy2/xjlS/5fGOKKOkM817PoMG8l5cfS9yjJ0jOd9jml1fJ8yo/Ypyg/Jm6ktHoucvWxYfb5cpSzAVdX9Txd/f1htXy5oV5vLHqlpjsog65WaCtf6zOqzvFhkPflIHHvxLOfb6cMUW5LSrJ+P89ependDPE+pFzg5NYq1qT7KzX15HXV/pqqju1HKZeI/RPl83wu5fP8fXS4mpa3Zm5RHTBJkiSpEfYhlSRJUqNMSCVJktQoE1JJkiQ1yoRUkiRJjRp30z6tvfbaOWnSpKbDkCRJ6nu//OUv/5KZ6wxXbtwlpJMmTWLmzJlNhyFJktT3ImLIy/gO8JS9JEmSGmVCKkmSpEaZkEqSJKlRJqSSJElqlAmpJEmSGmVCKkmSpEaNu2mfJEnS0u/hhx/m/vvv5+mnn246FHWw3HLLMXHiRFZdddWe1GdCKkmSligPP/ww9913H+uvvz4rrbQSEdF0SGqRmTz++OPMmTMHoCdJqafsJUnSEuX+++9n/fXXZ+WVVzYZXQJFBCuvvDLrr78+999/f0/qNCGVJElLlKeffpqVVlqp6TA0jJVWWqlnXSpMSCVJ0hLHltElXy+PkQmpJEmSGmVCKkmSpEY5yl6SJC0dpk1tdvu7n9Ds9scxW0glSZJG0VlnnUVEdLxdffXVteuZPXv2gmWTJk3ikEMO6X3QY8wWUkmSpDFwwQUXsMEGGyy0bMstt2womiWLCakkiRMuv2VMtzd1L7+E1X+23nprNt1006bDWCJ5yl6SJKlhc+fO5X3vex/rr78+K6ywAltssQWnn35602GNGVtIJUmSxsD8+fOZN2/egvsRwYQJE3j44YfZaaedePzxxznuuOPYZJNNmDZtGocffjhPPvkkRx55ZINRjw0TUkmSpDGwxRZbLHR/xx13ZMaMGZx88snceeed/Pa3v2WzzTYDYNddd+Whhx7iU5/6FIcffjjLLju+U7bx/ewkSZKWEN/5zncWGtT03Oc+F4Arr7yS7bbbjk022WShFtTdd9+dM844g1tuuYWXvexlYx7vWDIhlSRJGgNbbbVVx0FN999/P7NmzWK55Zbr+LgHHnhgtENrnAmpJElSg9Zaay0mTpzIySef3HH95ptvPsYRjT0TUkmSpAbtsccenHLKKWy00UZMnDix6XAaYUIqSZLUoKOOOorzzjuPV73qVRx11FFsvvnmPProo/zud7/jZz/7GZdccknTIY46E1JJkrR0GKfXkl9ttdX4xS9+wfHHH8/nPvc55syZw+qrr87mm2/Ofvvt13R4YyIys+kYemry5Mk5c+bMpsOQpKWKV2rSkuTWW2/lxS9+cdNhqAvDHauI+GVmTh6uHq/UJEmSpEaZkEqSJKlRJqSSJElqlAmpJEmSGmVCKkmSpEaZkEqSJKlRJqSSJElqlAmpJEmSGmVCKkmSpEZ56VBJkrRUGOsrirXzCmOjxxZSSZKkUXTWWWcRER1vV199ddPhLWL69OlEBNOnTx+zbdpCKkmSNAYuuOACNthgg4WWbbmlra5gQipJkjQmtt56azbddNOmw1giecpekiSpYbfddhtvetObWH311VlppZXYfvvtufLKKxcqc8ghhzBp0qRFHjtlyhSmTJmy4P7AKfdLL72UI444grXXXpu1116bgw8+mIceemihx86dO5eDDjqIVVddldVXX523v/3ti5QZCyakkiRJY2D+/PnMmzdvwW3+/PkA/PnPf2annXbi17/+Naeeeirnn38+q6++OnvttRff//73R7y9D37wg0QE55xzDsceeywXXXQRH/zgBxcq8+Y3v5nLLruME088kfPOO49ll12WI488crGe50h4yl6SJGkMbLHFFgvd33HHHZkxYwYnnXQSDz74INdcc82CU/qvf/3r2XLLLZk6dSp77rnniLa38847c8oppwDwute9jttuu40zzjhjwSCrq666ihkzZvDtb3+bAw44AIDdd9+dPffck7vvvnsxnml9JqSSJElj4Dvf+c5Cg5qe+9znAvDTn/6U7bfffqH+pRMmTODAAw/k+OOP5+GHH2bVVVetvb299tprofsvfelLefLJJ7nvvvt43vOexzXXXMOECRPYb7/9Fip3wAEHLNJdYLSZkEqSJI2BrbbaquOgpr/+9a9ss802iyx/3vOeR2by4IMPjighXXPNNRe6v8IKKwDwxBNPAHDPPfewxhprsNxyyy1Ubt111629rcVlH1JJkqQGrbnmmtx7772LLL/33nuJCNZYYw0AVlxxRZ566qlFyj3wwAMj2u56663Hgw8+yNNPP73Q8vvuu29E9S0OE1JJkqQGvfrVr+baa69l9uzZC5bNnz+f8847j2222WZB6+jGG2/Mfffdx9y5cxeUu/3227nttttGtN0ddtiB+fPnc9FFFy20/Nxzzx1RfYvDhFSSJKlBRx11FKuvvjq77bYb55xzDpdddhlvfOMb+f3vf88JJ5ywoNz+++9PRHDwwQczbdo0zj77bPbZZx/WXnvtEW13t912Y6edduK9730vp556KtOmTePQQw/lpptu6tVT65p9SCVJ0lJhvF5L/vnPfz4zZszgYx/7GIcffjhPPvkkW2+9NZdffjl77LHHgnKbbropF154Iccccwz77rsvL3rRizjppJM48cQTR7ztiy++mA984AMcffTRTJgwgb333ptTTz2VfffdtxdPrWuRmWO6wdE2efLknDlzZtNhSNJS5YTLbxnT7Y3XxEK9ceutt/LiF7+46TDUheGOVUT8MjMnD1ePp+wlSZLUKBNSSZIkNcqEVJIkSY0yIZUkSVKjTEglSdISZ7wNuh6PenmMTEglSdISZbnlluPxxx9vOgwN4/HHH1/ksqMjZUIqSZKWKBMnTmTOnDk89thjtpQugTKTxx57jDlz5jBx4sSe1OnE+JIkaYkycKnMP//5z4tcZ11LhuWWW4511113wbFaXCakkiRpibPqqqv2LNnRks9T9pIkSWqUCakkSZIa5Sl7SVoCjfW15SWpSbaQSpIkqVEmpJIkSWqUCakkSZIaZUIqSZKkRi1WQhoRy0bEfhHxnoh43ggev0dE3BYRsyLi40OU2y8iMiImL068kiRJWvJ0nZBGxOcj4vqW+wFcDZwPfBX4bUS8sEZ9E4DTgD2BLYEDI2LLDuWeC3wQuK7buiVJkrT0qNNCugfws5b7bwR2Br4AHFQtG7SVs4NtgVmZeUdmPgWcC+zTodyngc8BT9SoW5IkSUuJOgnphsAfWu6/EfhjZn48M88F/hvYpUZ96wN3tdy/u1q2QES8AtgwMy8fqqKIOCwiZkbEzLlz59YIQZIkSU2rk5AuD8xruf8ayin7AXcA6/UiKICIWAY4CfjIcGUz8/TMnJyZk9dZZ51ehSBJkqQxUCchvQvYASAiXgK8APhJy/qJwCM16ptDaXUdsEG1bMBzga2A6RExG9geuNSBTZIkSeNLnUuHngt8IiImAi8BHgauaFm/DXB7jfquBzaLiE0oiegBPNsXlcz8G7D2wP2ImA78a2bOrLENSZIkLeHqJKSfobRo7gv8DXh7Zj4EEBGrAXsDX+q2ssycFxFHANOACcCZmXlzRBwPzMzMS2vEJqndtKlju73dTxjb7UmSxo2uE9LMfBJ4V3Vr93dK/9FH62w8M69g4VZWMvOTg5SdUqduSZIkLR3qzEN6ZkRs12ldZj4DbA6c3qvAJEmS1B/qDGo6BBhq4vtNgHcsVjSSJEnqO728lv0qwNM9rE+SJEl9YMg+pBGxETCpZdEWEbFzh6JrAocDs3oXmiRJkvrBcIOa3gkcC2R1m1rd2gXwTFVekiRJ6tpwCel3gdmUhPNMyqCla9rKJGVC/Osz8y4kSZKkGoZMSDPz18CvASJiY+CizLxpLAKTJElSf6gzD+mnRjMQSZIk9ac6V2oiIgLYFdgMWItyKr9VZuanexSbJEmS+kDXCWlEbEbpU7oFiyaiAxIwIZUkSVLX6rSQnkKZGP9jwI+AB0YlIkmSJPWVOgnpq4AvZ+YXRysYSZIk9Z86V2p6EvjjaAUiSZKk/lQnIZ0G7DhagUiSJKk/1UlIPwzsEBEfiYjlRysgSZIk9Zc6fUh/DqwCfB74bET8GZjfViYz84W9Ck6SJEnjX52E9E+UaZ0kSZKknqlzpaYpoxiHJEmS+lSdPqSSJElSz5mQSpIkqVF1Lh36DMP3Ic3MrNMvVZIkSX2uTvL4TRZNSJelXE50O+A3wI09ikuSJEl9os6gpkMGWxcR/whcChzeg5gkSZLUR3rShzQzfwH8D/C5XtQnSZKk/tHLQU1/AP6hh/VJkiSpD/QyIZ0CPN7D+iRJktQH6oyyf/sgq9YEdgX2BL7ei6AkSZLUP+qMsj+LMso+OqybR0lGj+pBTJIkSeojdRLS13RYlsBfgT9m5qO9CUmSJEn9pM60Tz8ZzUAkSZLUn0Z0VaWIWAvYpLr7x8x8oHchSZIkqZ/UGmUfES+PiJ8A9wPXVbf7I2J6RLxsNAKUJEnS+FZnlP1WwAxgReAS4OZq1UuANwI/i4h/zMybB6lCkiRJWkSdU/bHA08DO2bmb1pXVMnqT6sy+/UuPEmSJI13dRLSnYHT2pNRgMwhM/tYAAAgAElEQVS8KSK+AryvZ5FJWrpMmzq229v9hLHdniRp1NTpQ7oKcO8Q6++pykiSJEldq5OQ3gG8YYj1b6jKSJIkSV2rk5B+E9g9Is6JiJdExITqtlVEnA28jnI1J0mSJKlrdfqQfhF4BXAA8FbgmWr5MpTLiZ4P/EdPo5MkSdK4V+dKTfOBt0bEGcC+PDsx/h3AdzPz6lGIT5IkSeNc7Ss1ZeZVwFWjEIskSZL60LB9SCNij4iYMkyZKRHxup5FJUmSpL4xZEIaEa8FLgeeP0w96wHfj4hX9SowSZIk9YfhWkjfCdyUmecMVSgzvw38Bnh3rwKTJElSfxguId0J+G6XdV1SlZckSZK6NlxCuh4wu8u67mT4U/uSJEnSQoZLSJ8CVuqyrpWApxcvHEmSJPWb4RLSPwLbd1nXdlV5SZIkqWvDJaTTgP0j4kVDFarWvwW4sleBSZIkqT8Ml5B+mXLa/qqI2K1TgYjYFfgB8GRVXpIkSerakFdqysw/R8RBlOvUXxkRdwE3Ag8DzwW2BjYCngD+KTPvGeV4JUmSNM4Me+nQzLw8Il4JfBp4PbB3y+onKdNCfTIzbx6dECVJkjSedXUt+8y8BdgvIlYANgNWpbSS/iEznxzF+CRJkjTOdZWQDqiSz5tGKRZJkiT1oeEGNUmSJEmjyoRUkiRJjTIhlSRJUqNq9SGVtBimTW06AkmSlki2kEqSJKlRXSWkEfGciLg9Ij402gFJkiSpv3SVkGbmI8BawCOjG44kSZL6TZ1T9tcCk0crEEmSJPWnOgnpx4G3RMQ7IyJGKyBJkiT1lzqj7E8CHgTOAD4fEbcDj7WVyczcpVfBSZIkafyrk5C+AEjgT9X9dXsfjiRJkvpN1wlpZk4axTgkSZLUp5yHVJIkSY2qfaWmiJgE7Eo5ZX92Zs6OiOWB5wH3ZuZTPY1QkiRJ41qtFtKI+BzwB+B04HhKv1KAFYFbgPfXrG+PiLgtImZFxMc7rH9fRPw2Im6MiBkRsWWd+iVJkrTk6zohjYj3Av8POA14HbBg6qfMfBi4FHhjjfomVHXtCWwJHNgh4TwnM1+amVsDn6eM9JckSdI4UqeF9P3AdzLzQ8CvOqz/DbB5jfq2BWZl5h3Vaf5zgX1aC1SJ7oBVKKP8JUmSNI7U6UP6IuC/hlg/F1i7Rn3rA3e13L8b2K69UET8C/BhYHngtTXqlyRJ0lKgTgvpE5RWysFsDDy0eOEsKjNPy8wXAh8DjulUJiIOi4iZETFz7ty5vQ5BkiRJo6hOQvp/wJs6rYiIFYG3AT+vUd8cYMOW+xtUywZzLrBvpxWZeXpmTs7Myeuss06NECRJktS0OgnpF4AdIuJbwMuqZc+LiN2B6ZSE8os16rse2CwiNqmmjTqAMjBqgYjYrOXuXpQR/pIkSRpH6lyp6eqIOBw4GTioWvyt6u9TwHsy85oa9c2LiCOAacAE4MzMvDkijgdmZualwBERsSvwNPAg8I5u65ckSdLSodbE+Jl5ekRcCuwPbEGZ+ukPwPmZOdTp9sHquwK4om3ZJ1v+/2DdOiVJkrR0qX2lpsy8FzhlFGKRpO5Nmzq229v9hLHdniT1kdoJKUBEbM6zV2m6IzNv611IkiRJ6ie1EtKIeC2ldXSLtuW/Az6QmT/sYWySJEnqA10npFUyeiXwJPA1yrXrAV4CHAh8PyL2yMwf9TxKSZIkjVt1WkhPBO4Dtm8fwBQRnwauBU4AduhdeJKk8eiEy28ZvlAPTd1ryzHdnqR66sxD+jLgq51G02fm3cBXgZf3KjBJkiT1hzoJ6d+Avw+x/mFG4dKhkiRJGt/qJKQXAAdGxCKn+SNiOUo/0gt6FZgkSZL6w6B9SCNio7ZF/w38I/DTiPgS8Ltq+YuBoyhXW/rv0QhSkiRJ49dQg5pmA9m2LKq/5w6y/CZKYipJkiR1ZaiE9HgWTUglSZKknho0Ic3M48YwDkmSJPWpOoOaJEmSpJ6rfS37iNgM2AxYi2f7ji6Qmd/sQVyStEQZ64ncJamf1Ll06HrAN4BdBhZ1KJaACakkSZK6VqeF9HTgNcCXgZ8BD45KRJIkSeordRLS1wInZ+a/jlYwkiRJ6j91BjU9AswarUAkSZLUn+okpJcBu45WIJIkSepPdRLSjwCbRMSXIuIFEdFpUJMkSZJUS9cJaWY+RBll/wHgD8C8iJjfdps3WoFKkiRpfKoz7dNHgc8A9wH/h6PsJUmS1AN1RtkfCUwH9sjMp0cnHEmSJPWbOn1I1wTONxmVJElSL9VJSH8NbDRagUiSJKk/1UlIpwKHRcTk0QpGkiRJ/adOH9K3AXOAayPiGuAOYH5bmczMd/UqOEmSJI1/dRLSQ1r+37G6tUvAhFSSJEld6zohzcw6p/clSZKkrphkSpIkqVEmpJIkSWpUnSs1/aiLYpmZuyxGPJIkSeozdQY1vYAyaKn98etRWlr/Ajzao7gkSZLUJ+oMaprUaXlErAB8GHgn8OrehCVJkqR+sdh9SDPzycz8DHAdcNLihyRJkqR+0stBTTOA3XtYnyRJkvpALxPSTYDle1ifJEmS+kCdUfYbDbJqTWBX4APA9B7EJEmSpD5SZ5T9bBYdZT8ggNsoSakkSZLUtToJ6fEsmpAm8Ffg98DVmflMrwKTJElSf6gz7dNxoxiHJEmS+pSXDpUkSVKjhmwhjYhX1K0wM28YeTiSJEnqN8Odsp/J4AOZOsku6pQkSZIWGC557DSQqZPXA69c/HAkSZLUb4ZMSIcbyBQRrwQ+R0lGHwBO6FlkkiRJ6gsjGtQUEZtGxAXAtcC2wGeAF2bml3sZnCRJksa/Wv09I2IicBzwLkoy+3Xg2My8p/ehSZIkqR90lZBGxCrAR4GjgOcA3wWOzszbRjE2SZIk9YHhpn1aFjgcOAZYB5gBfCwzrxmD2CRJktQHhmsh/R2wCXAL8K7MvGz0Q5IkSVI/GS4hfQFl2qdNgHMiYrj6MjNX60VgkiRJ6g/DJaQ/pd7E+JIkSVItw81DOmWM4pAkSVKfGtE8pJIkSVKvmJBKkiSpUSakkiRJapQJqSRJkhplQipJkqRGmZBKkiSpUSakkiRJatSg85BGxEYjqTAz/zTycCRJktRvhpoYfzYju0rThJGFIkmSpH40VEJ6PIsmpHsDWwNXAbdUy14C7ALcCHyv1wFKkiRpfBs0Ic3M41rvR8RBwCbAP2TmjW3rXgH8EPj9KMQoSZKkcazOoKaPA6e2J6MAmXkDcBpwdK8CkyRJUn+ok5BuBtw/xPr7qjKSJElS14bqQ9ruHuDNEXFaZi7UtzQilgH2A+7tZXCStMSYPWNstzdpp7HdniQ1qE4L6deAKcC0iNgjIjapbnsC04BXAaePQoySJEkax+q0kH4WWBc4kjKqvt1pmfmZOhuPiD2AkylTRZ2RmZ9tW/9h4N3APGAucGhm3llnG5IkSVqydZ2QVqfpPxQRXwH2pYy4B7gDuDQzb6uz4YiYQBkItRtwN3B9RFyambe0FPsVMDkzH4uIw4HPA2+tsx1JkiQt2eq0kAKQmb+nJIaLa1tgVmbeARAR5wL78Oz8pmTmj1vKXwsc3IPtSpIkaQlS+1r2EbFKROwaEf8cEesuxrbXB+5quX93tWww7wK+P0hMh0XEzIiYOXfu3MUISZIkSWOtVkJanTafA/wA+CblKk1ExMSIeCIi3tP7ECEiDgYmA1/otD4zT8/MyZk5eZ111hmNECRJkjRKuk5II2I/Sp/PH1MGGsXAusy8H7iS0re0W3OADVvub1Ata9/ursBUYO/MfLJG/ZIkSVoK1Gkh/X/AjzPzTcAlHdbPBLaqUd/1wGbV1FHLAwcAl7YWiIhtgK9SktGhJuWXJEnSUqpOQvpS4DtDrL8HmNhtZZk5DziCMofprcD5mXlzRBwfEXtXxb4APAe4ICJujIhLB6lOkiRJS6k6o+znM3QC+3zg0Tobz8wrgCvaln2y5f9d69QnSZKkpU+dFtJfA7t3WlFdOnR/yml4SZIkqWt1EtJTgT0j4tPAmgOPj4jNgQsoI+7/s8fxSZIkaZyrc6Wm8yLipZQR70dXi6+kjLYP4LjM7DhPqCRJkjSYWldqysxjIuJi4J+BLSiJ6B+Ab2XmzFGIT5IkSePcSC4degNwwyjEIkmSpD5UZ2L8T0bEYUOsf2lEfHKw9ZIkSVIndQY1HQf8V0RcEBErdlj/MuDYnkQlSZKkvlHrWvbAj4A3AT+JiK4nwZckSZIGUzch/R/K9epfDPxfRNS5VKgkSZK0iLoJKZl5GbAzZUDUzyNiz55HJUmSpL5ROyEFyMwbge2A24FLI+LInkYlSZKkvlF72qcBmTknInYCzgW+DNzSs6iksTJtatMRSJLU90bUQjogMx8D9gFOoVw6VJIkSaqlTgvpJsDc9oWZmcCHIuJKYN1eBSZJUq+ccPnYnsSbuteWY7o9aWlX51r2dw6z/srFD0eSJEn9ZtCENCI2AsjMP7XeH85AeUmSJKkbQ7WQzgaeiYiVM/Op6n52UeeEHsQlSZKkPjFUQno8JQGd13ZfkiRJ6plBE9LMPG6o+5IkSVIvLNa0T5IkSdLiMiGVJElSo4YaZf8M9fuMZmaO+OpPkiRJ6j9DJY/fxEFMkiRJGmVDDWo6ZAzjkCRJUp+yD6kkSZIaZUIqSZKkRtVKSCNix4i4LCLmRsS8iJjfdps3fC2SJEnSs7pOSCNiZ+DHwHbAddVjfwxcDwRwE/CtUYhRkiRJ41idFtKpwD3AlsAh1bITM3N7YA9gE+CMnkYnSZKkca9OQrotcEZmzgWeaX18Zv6A0jr66d6GJ0mSpPGuTkK6AjCn+v/J6u9zW9bfCPxDL4KSJElS/6iTkN4DbACQmY8CDwFbtazfAHBQkyRJkmqpc5nP64EdW+7/ADgqIu6kJLZHUAY7SdKoO2HWxk2HIEnqkTotpF8H/hIRK1X3/w14HDgLOJNyGv+jPY1OkiRJ417XLaSZeRVwVcv9OyLiRcAuwHxgRmb+rfchSpIkaTyrc8p+EVVf0kt7FIskSZL6kJcOlSRJUqPqXjr0oIj4eUTc3+GyoV46VJIkSbV1fco+Io4BPgXcB/wCeHC0gpIkSVL/qNOH9P3AdGCPzHx6dMKRJElSv6lzyn5V4HyTUUmSJPVSnYT0V8CGoxWIJEmS+lOdhPQY4H0Rsc1oBSNJkqT+U2di/J9ExLuAayPiWmA2ZUL8tmL5rh7GJ0mSpHGuzij77YBvAMsBr6pu7RIwIZUkSVLX6pyyPxl4CtgHWDMzl+lwmzA6YUqSJGm8qjPt08uA4zLze6MVjCRJkvpPnRbS+yktpJIkSVLP1ElIzwQOjog6raqSJEnSkOoklzOAN1BG2X8F+COLjrInM3/ao9gkSZLUB+okpFe3/H8GZUR9q6iWObBJkiRJXauTkB7KokmoJEmStFjqTIx/1ijGIUmSpD7V1aCmiHhORNweER8a7YAkSZLUX7pKSDPzEWAt4JHRDUeSJEn9ps60T9cCk0crEEmSJPWnOgnpx4G3RMQ7IyJGKyBJkiT1lzqj7E8CHqRM+fT5iLgdeKytTGbmLr0KTpIkSeNfnYT0BZRpn/5U3V+39+FIkiSp39SZ9mnSKMYhSZKkPlWnD6kkSZLUc3VO2QMQEasCu1JO4QPcAVyVmX/vZWCSJEnqD7US0oh4N/AfwHMo166H0q/0kYj4cGZ+vcfxSZIkaZzrOiGNiL2B0yktop8Abq5WvQQ4Ejg9Iu7PzO/1PEpJkiSNW3VaSD8K3ApsV125acAPI+J/KBPnfwwwIZUkSVLX6iSkLweOb0tGAcjMv0fENygtp5KkxTV7xthub9JOY7s9SWpRZ5T9cFdnysUJRJIkSf2pTkL6a+CQiFilfUVEPAc4pCojSZIkda1OQvoF4MXADRHxLxHxmup2BPBLYIuqTNciYo+IuC0iZkXExzus3zkiboiIeRHxT3XqliRJ0tKhzpWavlsln58DTuHZU/QBPAockZmXdFtfREwATgN2A+4Gro+ISzPzlpZif6K0vP5rt/VKkiRp6VJrHtLM/EpEnENJIjepFg9MjP+3mtveFpiVmXcARMS5wD7AgoQ0M2dX656pWbckSZKWErWv1JSZDwEX9GDb6wN3tdy/G9huJBVFxGHAYQAbbbTR4kcmSZKkMTMurmWfmadn5uTMnLzOOus0HY4kSZJqGLKFNCIurVlfZuY+XZadA2zYcn+DapkkSZL6yHCn7N9Qs746c5FeD2wWEZtQEtEDgINqbk+SpCXOCZffMnyhHpm615Zjti1ptAx5yj4zlxnuBryGklwC3NPthjNzHnAEMI1ySdLzM/PmiDg+IvYGiIhXRsTdwP7AVyPi5hE8R0mSJC3Bag9qGhARW1GmgNoD+DvlsqEn1akjM68Armhb9smW/6+nnMqXJEnSOFU7IY2IDYFPA/8MzAf+E/j3zHygx7FJkiSpD3SdkEbEGsBU4P3ACsC3gWMG5gqVJEmSRmLYhDQiVgA+BHwMWB24CvhYZt44yrFJkiSpDww5qCki3gXMAk4Ebgd2y8zdTUYlSZLUK8O1kH6NMpXTTOB84OUR8fIhymdmfqlXwUmSJGn866YPaQCvrG7DScCEVJIkSV0bLiF9zZhEIUmSpL41ZEKamT8Zq0AkSZLUn4Yc1CRJkiSNNhNSSZIkNcqEVJIkSY0yIZUkSVKjTEglSZLUKBNSSZIkNaqbifGlsTNtatMRSJKkMWYLqSRJkhplQipJkqRGmZBKkiSpUSakkiRJapQJqSRJkhrlKHtJPXHCrI2bDkGStJSyhVSSJEmNMiGVJElSo0xIJUmS1CgTUkmSJDXKhFSSJEmNMiGVJElSo0xIJUmS1CgTUkmSJDXKhFSSJEmNMiGVJElSo7x0qCQJZs8Y2+1N2mlstydpiWYLqSRJkhplQipJkqRGmZBKkiSpUSakkiRJapQJqSRJkhplQipJkqRGmZBKkiSpUSakkiRJapQJqSRJkhplQipJkqRGeelQSZKWYidcfsuYbm/qXluO6fbUH2whlSRJUqNsIZXGqRNmbdx0CJIkdcUWUkmSJDXKhFSSJEmNMiGVJElSo+xDKkkae7NnjO32Ju00ttuTVIstpJIkSWqUCakkSZIaZUIqSZKkRpmQSpIkqVEmpJIkSWqUCakkSZIaZUIqSZKkRpmQSpIkqVEmpJIkSWqUCakkSZIaZUIqSZKkRnkte0nS+Dd7xthub9JOY7s9aSlnQiqNkRNmbdx0CJIkLZE8ZS9JkqRGmZBKkiSpUSakkiRJapQJqSRJkhrloCYN6YTTTh/T7U3ddEw3J0mjYyxH9TuiX+OACan6miPfJameEy6/ZUy3N3WvLcd0e2qGp+wlSZLUqEZbSCNiD+BkYAJwRmZ+tm39CsA3gX8AHgDempmzxzpOjR1bLCWpJif91zjQWEIaEROA04DdgLuB6yPi0sxsPRfwLuDBzNw0Ig4APge8deyjlSRJwJgnwCdcPqabG/MuAnaBKJpsId0WmJWZdwBExLnAPkDrkdkHOK76/0Lg1IiIzMyxDHSJMm3qGG/QFktJUoPGugWYMU7Yxvvz61I0ldtFxD8Be2Tmu6v7bwO2y8wjWsrcVJW5u7p/e1XmL211HQYcVt3dHLhtDJ7CgLWBvwxbSks6j+P44HEcHzyO44fHcnxYnOO4cWauM1yhcTHKPjNPB8Z2fqJKRMzMzMlNbFu943EcHzyO44PHcfzwWI4PY3EcmxxlPwfYsOX+BtWyjmUiYllgNcrgJkmSJI0TTSak1wObRcQmEbE8cABwaVuZS4F3VP//E/Cjvu4/KkmSNA41dso+M+dFxBHANMq0T2dm5s0RcTwwMzMvBb4OfCsiZgF/pSStS5pGugqo5zyO44PHcXzwOI4fHsvxYdSPY2ODmiRJkiTwSk2SJElqmAmpJEmSGmVC2gMR8YWI+F1E/CYivhMRqzcdk7oXEXtExG0RMSsiPt50PKovIjaMiB9HxC0RcXNEfLDpmDRyETEhIn4VEZc1HYtGJiJWj4gLq+/GWyNih6ZjUn0RcVT1mXpTRHw7IlYcrW2ZkPbGVcBWmfky4PfA0Q3Hoy61XMJ2T8rlKw6MiCXzMhYayjzgI5m5JbA98C8ex6XaB4Fbmw5Ci+Vk4MrM3AJ4OR7PpU5ErA98AJicmVtRBqCP2uByE9IeyMwfZOa86u61lDlVtXRYcAnbzHwKGLiErZYimXlPZt5Q/f93ypff+s1GpZGIiA2AvYAzmo5FIxMRqwE7U2bKITOfysyHmo1KI7QssFI1F/zKwJ9Ha0MmpL13KPD9poNQ19YH7mq5fzcmMku1iJgEbANc12wkGqEvAx8Fnmk6EI3YJsBc4H+qrhdnRMQqTQelejJzDvBF4E/APcDfMvMHo7U9E9IuRcTVVR+K9ts+LWWmUk4dnt1cpFL/iojnABcBH8rMh5uOR/VExBuA+zPzl03HosWyLPAK4L8ycxvgUcD++UuZiFiDcsZwE+D5wCoRcfBobW9cXMt+LGTmrkOtj4hDgDcAu3g1qaVKN5ew1VIgIpajJKNnZ+bFTcejEdkR2DsiXg+sCKwaEf+bmaP2JahRcTdwd2YOnKW4EBPSpdGuwB8zcy5ARFwM/CPwv6OxMVtIeyAi9qCcYto7Mx9rOh7V0s0lbLWEi4ig9Fe7NTNPajoejUxmHp2ZG2TmJMp78Ucmo0ufzLwXuCsiNq8W7QLc0mBIGpk/AdtHxMrVZ+wujOLgNFtIe+NUYAXgqnLMuDYz39dsSOrGYJewbTgs1bcj8DbgtxFxY7Xs3zLzigZjkvrZkcDZ1Q/9O4B3NhyPasrM6yLiQuAGSnfEXzGKlxD10qGSJElqlKfsJUmS1CgTUkmSJDXKhFSSJEmNMiGVJElSo0xIJUmS1CgTUkmSJDXKhFSSJEmNMiGV1DPVFa++GxFzIyIj4qymY1oSRcSUav8c0sC2t4qIeRGx25IQz+Lo9Fz6QUTsExFPRcRmddZJSzITUqnHWr7cMyJOHaTMxOpLIyNi+hiHOJrOAl4NfI5y5aSvDlZwrPZTRGwdEcdFxKSRPH6sjGGcJwE/z8yrRnk7Y2E8PZeuZeYlwG8p77Ou10lLMhNSafQ8ARwUESt0WPc2ICiXYxsXquf5KuBbmfnFzPzfzLymi4eO9n7aGjgWmLQYdfTaT4GVgG+1LBv1OCNiB2A3SiK3VBtPz2WETgbeFBEvqblOWiKZkEqj5zvAGsA+Hda9E7gCeHJMIxpd61KSx7/WfFxf7KeImBARKwNk5jOZ+URmzh/jMN4P/IWyT5d2o/JcWo/TEu5i4DHgfTXXSUskE1Jp9NwA/IaSVC0QEdsCLwH+p/0BEfHciPj3iLguIv4SEU9GxKyI+Gz7l2RErFid4r0tIh6LiIci4rcR8YU6ZYYTEWtHxGkRcVd1+vyu6v5aLWXOAu6s7h7bcip+ShebqL2fqvUrRMS/RcTNEfFE9dy+FxHbtJQ5ruXxP26J66w69dTZF1W5Q6rt7BoRn4iI2yktwW+p1i/UZ7PXcQ6yv5YF9gWuzsynu3xMV8+3KjspIi6KiIer2yVR+hTP7nW3lOGeS7exdHGc6rwfB+raJSI+GRF3RsTj1WO3r8q8OiJmRMSjEXFPRHyirY6u36+Z+QjwM+Cf6qyTllTLNh2ANM6dCZwUEetn5pxq2aHA/cBlHcqvD7wbuAg4h3Kq+tXAR4FtgN1byp5W1fVNymnLZYHNgNfWLDOoiFgN+AWwafVcbqjiOBx4bURsm5l/p/QVvRH4EqXF8+Kqilu72Q4191NELAdcCfwj5bT3qcBqwHuAn0fEzpk5s4pjPeAw4MSWeG7//+2da6wdVRXHf6svqFBDoQVDImkVhIb6wUcQDRBeATVKCkYTQOGikCAaIxR5fuERg2I1YqKC8mhIiSGQKiVRXikk/UAooSS2IS0CvTQoEWyLWCSpgcWHtU+ZTs+Zs/fpnJ6D/f+Snbmz9zp71tozO7PufqwprKekLaosAaYDvwfeBDb0sL81PRv4DLA/sLqPHKX2Jgd1FTFKfmvS/3jgcWC/nOsV0tOWAXXpdZ9K+mOHnwBTiWnzGcBi4BEzOw+4A/gdcA/h9N5gZhvdfVn6bWl/fRI43cyOcvf1BWVCjB/urqSk1GICTgQcuBw4iJhuviaVzQTeAJak823AE5XfzgCmd6nzxlTnMZW8LcCf++jSV6bP73+crntJLf97Kf/GSt68lHfdHminS9NvT6/V+WFgU012Isme2EWHknpK2qJzzQ3Ahxpsn2hbz4b2viDVcUamPiX23pzyzq3JdvL76lf4XDbZkq1Lxn0q6Y+dutYAMyr5Z6T8/wGfrdX9KvBkJa+ovwLfTHV/raRMSWkck6bshRgi7r4ZWEG8rADOIka27uwhv93TFKSZTTOz2WY2B3gsiXyuIv5v4GgzW9igQo5ME2cCrxOjOlVuS/lnDljvTpS2E/GyXQ88k6aV56R2mgE8ChxnZjMzLl1SzyBt8Vt3/2+GHm3q2Yu56Zi7xrfE3q8SztUfarJLMq9VSpMtg+jS9T4V9sdqXdsr56vS8SmvjGInmdXECGiH0v66OR0PLiwTYuyQQyrE8LkLOMLMjiOm41a7+3O9hM3sEjP7KzFiuIV4+T+RimdXRH+Yztea2YtmdrtFDMIphTJNzAc2uPtOu9zT+fPAxzLryaGknRYARxFtU0/fJqZM52Rcs6SeQdri+QwdcmjDXk9Hy7xmib3zgRfc/d2a7GvESPcOzOwbaR3lNjObrF80OX63mNmWtIbyDjPbt8CWbF0q9LxPBf2xw0u1625Nf27sIruVmB3oUNpfO/Z7YZkQY4fWkAoxfB4G/k6E9DmJWIPXFTO7DPg58AjwK+AfwHZiLdtSKv9EuvsDFjErv0ysazsV+A6wysxOTaM7fWVatHN3yW4n4mW7FrisQfZ4bfQAAAQySURBVOb1jGu2VU8v2hgdhXb07JQf2IpGg7OVWAN7CLEUoc41xP3/JPHsryCm239QkWnblq73qaQ/VugVOaFvRIUB+mvH/m73vqlMiLFDDqkQQ8bd3zGzu4GrgbfZdSqxyreASeBL1REeM/tij7q3AMuAZWZmxIaKK4gQSvflyjTwEnCkmU2rjpSlXc6foDYatDsUttPfiGnblfWRsG5Vt1TPsNuiLT17sS4dc7/gU2LvJHC4mU2pPbcHAwdUK/UUxN7MFvW47oXAFZ42t1lEILjPzC7198NkNdmSrUsGRf2xDQr76+HpuI5daSoTYuzQlL0Qe4ZbgeuBi939zQa5dwjHZMdUZHIArqoKWcRKrL/oHXg2nR6YI5Oh958IR+jCWv5FKf+PGXWUkNtOdwMfoceIoZkdUjndlo7d7C2pZ9ht0ZaevXiW2EF+bKY+JfY+SEQJOLsme3nmtQBIz+tHiYgNHdYAs9j5gwFNtrSiSyKrP7bBgP31WOCf7t4tekNTmRBjh0ZIhdgDuPsm4LoM0fuBm4C/mNlyYhf1OcQO3SqzgFfNbAXxwnqNWDv3XWJK9MFMmX7cDHwd+LWZfTrV8yliGnFDKm+Ngna6hfhKz8/M7GRgJeGgHAacQsSSPCnJPg28C1xrZrOBt4CN7v5UYT3Dbou29OxKGoFeDiwys33cvd/HBkrs/SnxnN5lET92PRFq6QtE8PrcdYyz0rG61vONWlk/W9rSBfL7YxsU9Vcz25+wa5eNf01lQowto97mr6T0/5aohDPKkK2HM5pKTFm/QGyieJl48S+gElKJ2F19E7FLd3OSnSReQEfkymTaMxf4DfAK8SJ+hYiXOKcmN6+q4zDbKeVNI9YVPk04b28RU9v3AKfVZM8HniPW/zmwdMB6cttigh4hnGq2TwxDz4Z2PIYuoYAa9MmyN8nOJ+Kp/odwlh9Ief+iSygjIrD9ZC3vgKTHkTUdHPh4ji0lumTcp6z+2K+u+r2s5C9lx0BoWX9Nz4oDC0vKlJTGNZm7NuAJIcTegpk9BOzn7sfvgWsdRDiBt7n7xbWyRcAv3X1eLX8T8CN3vzedn0aMVM722qdWS2xp0uWDiJmtIRz6s0rKhBhXtIZUCCH2LhYDn0+OXmv0iIPaWWv5aEVuagrjND1ObV8z26fym9uBq83sUDObSyzhWFp3RhNdbcnV5YNKcuYXAleWlAkxzmiEVAghxG5jZo8TU9priMGOU4CvEJ8fPaHjUJrZBBFztsrLnZHStGnoF8QO9ynE6Oj33f3ttnURQowPckiFEELsNma2GDiPWEs8k1hvuhy43tM37/dGXYQQecghFUIIIYQQI0VrSIUQQgghxEiRQyqEEEIIIUaKHFIhhBBCCDFS5JAKIYQQQoiRIodUCCGEEEKMFDmkQgghhBBipMghFUIIIYQQI+U9L8aoKj+pNTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115151dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(11,7))\n",
    "\n",
    "\n",
    "'''\n",
    "Here the new arguments to hist are as follows\n",
    "\n",
    "density  : By setting this true, this tells python to calculate \"the percentage\" of data within each bin\n",
    "           to convert from raw counts to what can be considered a \"probability density\" instead. This allows\n",
    "           both of our meteorite fall types to be on the same scale\n",
    "          \n",
    "histtype : This is a stylization parameter. \"stepfilled\" is simply telling Python that we want bars that look\n",
    "           like \"steps\" and for them to be colored in. \n",
    "           \n",
    "           you can also change this to ‘bar’, ‘barstacked’ or  ‘step' to see how the different plot styles \n",
    "           look. We note that some styles will affect the scaling. \n",
    "\n",
    "alpha    : This takes values from 0 -> 1 and are a measure of how transparent the traces are. \n",
    "'''\n",
    "\n",
    "plt.hist(data_for_plot, \n",
    "         bins = 20, \n",
    "         density = True, \n",
    "         histtype='stepfilled', \n",
    "         alpha = 0.55,\n",
    "         label = [\"Found\", \"Fell\"]) \n",
    "\n",
    "\n",
    "# The dollar signs allow us to use math symbols in the text. \n",
    "plt.xlabel(\"Mass of Meteorite (log$_{10}$(grams))\", size = 18)\n",
    "plt.ylabel(\"Normalized Number Counts\", size = 18)\n",
    "plt.title(\"Mass Distribution of Meteorite Observations\", size = 20)\n",
    "\n",
    "# The prop key word changes the 'proportions' of the legend. \n",
    "plt.legend(prop={'size': 16})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this new scale, it seems to be the case that the more massive the meteorite is, the less likely it is to be found.\n",
    "\n",
    "---\n",
    "\n",
    "## Caution\n",
    "Be aware of the scaling. It _appears_ to be much more likely that more massive meteorites are less likely to be found. But, keep in mind the blue histogram is only about 2.5% of all observed meteorites. \n",
    "\n",
    "---\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "The appearance that the more massive a meteorite is the less likely it is to be found seems counter intuitive. This is actually a result of both the effect of the atmosphere on large fast moving bodies, and a consequence of the definition of \"Found\" in this data set. \n",
    "\n",
    "In terms of the atmosphere, larger meteorites have a tendency to explode as they enter Earth's atmosphere. For example, in 2013 a rather large meteor exploded over Chelyabinsk Russia, and its fall can be seen in the YouTube video below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"768.0\"\n",
       "            height=\"432.0\"\n",
       "            src=\"https://www.youtube.com/embed/fBLjB5qavxY\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1156aa748>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This library allows us to embed YouTube in Jupyter. \n",
    "from IPython.display import YouTubeVideo\n",
    "\n",
    "YouTubeVideo('fBLjB5qavxY',width=1024*0.75, height=576*0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite all the video evidence of that meteor falling, if we look up this meteor in our data set, we will find something interesting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass (g)</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Chelyabinsk</td>\n",
       "      <td>57165</td>\n",
       "      <td>Valid</td>\n",
       "      <td>LL5</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>01/01/2013 12:00:00 AM</td>\n",
       "      <td>54.81667</td>\n",
       "      <td>61.11667</td>\n",
       "      <td>(54.816670, 61.116670)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name     id nametype recclass  mass (g)  fall  \\\n",
       "194  Chelyabinsk  57165    Valid      LL5  100000.0  Fell   \n",
       "\n",
       "                       year    reclat   reclong             GeoLocation  \n",
       "194  01/01/2013 12:00:00 AM  54.81667  61.11667  (54.816670, 61.116670)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landings[landings.name == 'Chelyabinsk']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite the many angles available to see the meteor, it was never found? Why is that? Well, that gets us to the point of semantics. This meteor was an asteroid approximately 20 meters in width with a mass of greater than 10000 tonnes entering the atmosphere. However, only about 1000 kg of the meteor to date have been recovered. As a result, this meteor is classified as \"fell\" instead of found. Additionally, the recorded mass is only approximately what has been recovered so far. \n",
    "\n",
    "\n",
    "There is a relationship between mass of the meteorite and if it is found. But, this relationship is primarily due to the the greater likelihood of a large meteorite to explode, and the definition of \"Found\" requiring that the majority of the body to be recovered. \n",
    "\n",
    "For more information about meteorite explosions and the Chelyabinsk meteor see \n",
    "\n",
    "1. [The Wikipedia article](https://en.wikipedia.org/wiki/Chelyabinsk_meteor)\n",
    "1. [This Science Alert Article](https://www.sciencealert.com/why-do-meteors-explode-when-they-reach-earth-atmosphere)\n",
    "\n",
    "Certainly, there are other factors relating to why certain meteorites are found and some are not, than the mass of the meteorite such as geography, or if it was reported or not. Regardless, by exploring the relationship with mass, we were able to discover an interesting trend hidden within the data. \n",
    "\n",
    "# Conclusion\n",
    "\n",
    "In this notebook we demonstrated how you might go about working with your data set  to tease out more interesting information in the data set. More importantly, we went through the steps to create a histogram and covered many potential problems you may encounter in doing so. We covered some common errors and more subtle problems when working with a data set with a large spread in values, and some solutions to those problems. We also covered how some interesting trends in data may have perfectly reasonable explanations that are less exciting than the data may lead us to believe. It is our hope that this tutorial series has left you feeling more confident when it comes to working with open data in Jupyter notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)"
   ]
  }
 ],
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